The experiences of female long-distance labour commuters from the City of Kelowna to the Regional Municipality of Wood Buffalo
Bibliographic record
Abstract
Research conducted on female long-distance labour commuters (LDLC) in male-dominated resource extraction industries is limited particularity within a Canadian context. Female LDLCs’ experiences may be d1istinct from male LDLCs in important ways that can create specific barriers and challenges for female workers. This thesis examines therefore the experiences of female LDLCs acknowledging ideologies of hegemonic masculinities and neoliberalism. This study is spatially situated in the City of Kelowna as a home community and within the Regional Municipality of Wood Buffalo as a work site. Drawing on principals of Poststructural Feminist Geography, this study was conducted as an exploratory case study utilizing a sequential mixed methods approach. Data from this study was analyzed through critical discourse analysis. Recruitment and data collection for this thesis took place between November 2015 and October 2016 through a survey with 9 female LDLCs and 5 in-depth interviews. The study reveals push/pull forces influencing female LDLCs’ decisions to participate in this type of work. Further, the results indicate that female LDLCs face many implicit vs. explicit barriers working in male-dominated resource extraction industries: constant need to prove abilities and competencies, increased individualism and competition from precarious work, conflicts between motherhood and resource extraction work, and hegemonic stereotypes. The study additionally demonstrates that participants cope by putting up with challenges through strength and endurance, adopting a work-centric mentality demonstrated through work ethic, proving competencies, and invisibility through avoidance or disengagement. This study yields policy and practical recommendations aimed at improving female LDLCs’ work experiences: greater family friendly policies, a need for increased diversity in all sectors of resource extraction, more social support, and effective avenues for support and reporting of discrimination or harassment. Recommendations originating from this study also contribute to broader discussion on gender and labour with acknowledgement of various existing systemic and structural inequalities imbued under the contours of neoliberal ideology and hegemonic masculinities. This research is not representative of the majority of female LDLC in Canada, but instead focuses on a few in-depth experiences that act as a starting point for understanding and challenging existing barriers based on embedded inequalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".